Real-Time Monocular 3-D Pose Tracking for Noncooperative Spacecraft in Close Range

Real-time pose tracking for noncooperative spacecraft is crucial for numerous space missions, such as debris removal and docking. Existing pose tracking methods either suffer from shadow occlusion and cluttered backgrounds in space or lack computational efficiency. This study proposes a real-time system for tracking the 6-degree-of-freedom pose of a noncooperative spacecraft for proximity operations using a single calibrated camera. The proposed method parameterizes the pose in the Lie group of SE(3) and exploits the 3D boundaries and 3D contour, which are automatically extracted from the 3D model of the spacecraft as geometric features (GFs). At each frame of the tracking process, the initial pose is predicted using an error state Kalman filter (ESKF). Using the initial pose, the tracking system searches the input image for the 2D edge points that are associated with the 3D GFs based on a conditional random field and then rapidly determines the spacecraft pose in SE(3) by minimizing the distances of the projected 3D GFs to their corresponding 2D edge points using the M-estimation and Newton’s method. The covariance of the optimized pose is estimated based on the Karush–Kuhn–Tucker condition for pose optimization. With the estimated pose covariance, the ESKF is developed based on second-order autoregression in SE(3) to produce the maximum a posteriori estimate of the pose and initialize the pose tracking at the next frame. A sufficient number of synthetic and real trials indicate that the proposed method outperforms the existing methods in terms of accuracy and efficiency. The experimental results reveal that the proposed method achieves a rotation angle error of approximately 0.5° and relative position error of approximately 0.6% and that the average runtime of the proposed method is approximately 0.02 seconds per frame.